cfDNA CNV Detection Using Segmentation and CBS
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Solution Overview
Problem
Current methods for detecting genetic copy number variations (CNVs) in nucleic acids, particularly in non-invasive prenatal testing (NIPT) and oncology, face challenges such as fragmentation, low concentration, background noise, and computational complexity, leading to inaccurate detection of small or low-abundance CNVs.
Innovation Solution
A method involving sequencing nucleic acids, mapping to a reference genome, performing global and focused segmentations, and using circular binary segmentation (CBS) to identify CNVs with improved specificity and sensitivity, particularly in regions like 22q11.2, combined with RNA analysis for comprehensive genomic insights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If cfDNA is used for NIPT, then non-invasive testing is achieved, but detection accuracy decreases due to fragmentation and low concentration
Solution Approach 1:
The patent applies segmentation by dividing the genome into specific regions of interest (ROIs) and further into sub-regions for analysis. This allows focused analysis of CNVs in critical genomic areas using cfDNA, improving detection accuracy for small CNVs while maintaining non-invasive testing. The segmentation process creates a hierarchical structure (chromosomes → regions → sub-regions) that enables precise localization and quantification of copy number variations.
Solution Approach 2:
The patent implements local quality by applying different analysis strategies to different genomic regions. High-confidence ROIs with known CNV associations receive focused, intensive analysis with customized parameter sets, while other regions use standard analysis. This allows optimization of detection sensitivity for clinically relevant regions without compromising overall assay performance, directly addressing the low concentration and fragmentation challenges of cfDNA.
2Measurement precision
If genome-wide sequencing is performed, then comprehensive CNV detection is achieved, but computational complexity and cost increase
Solution Approach 1:
The patent extracts and focuses analysis on specific regions of interest (ROIs) that are most likely to contain clinically relevant CNVs. By taking out only the critical genomic regions for detailed analysis rather than processing the entire genome uniformly, the method reduces computational complexity and data processing requirements while maintaining comprehensive detection capability for medically important CNVs.
Solution Approach 2:
The patent applies partial action by performing enhanced, focused analysis on high-confidence ROIs where CNVs are most likely to occur, rather than applying uniform genome-wide analysis. This allows concentrated computational resources to be applied where they provide maximum diagnostic value, achieving comprehensive detection of clinically relevant CNVs with reduced overall computational burden.
3Productivity
If standard segmentation methods are used, then general CNV detection is achieved, but detection of small CNVs in specific regions deteriorates
Solution Approach 1:
The patent implements multi-level segmentation by first dividing the genome into chromosomes, then into regions of interest based on CNV frequency and clinical relevance, and finally into sub-regions for detailed analysis. This hierarchical segmentation enables the method to maintain high throughput for general CNV detection while simultaneously achieving high precision for small CNVs in specific regions through focused sub-region analysis with customized parameters.
Solution Approach 2:
The patent applies local quality by using customized analysis parameters and thresholds specific to each ROI and sub-region, rather than uniform parameters across the entire genome. This allows optimization of detection sensitivity for small CNVs in clinically critical regions while maintaining efficient processing for other regions, thereby improving both productivity and precision simultaneously.
4Measurement precision
If focused analysis on specific regions is performed, then detection resolution is improved, but detection of other CNVs is lost
Solution Approach 1:
The patent maintains adaptability and versatility through hierarchical segmentation that preserves the complete genomic structure. By organizing analysis into chromosomes, regions, and sub-regions, the method enables focused high-resolution analysis of specific ROIs while maintaining the framework for genome-wide detection. The segmented structure allows flexible switching between focused and comprehensive analysis modes without losing detection capability for other CNVs.
Solution Approach 2:
The patent implements multi-functionality by designing a unified analysis framework that can perform both focused high-resolution analysis of specific ROIs and comprehensive genome-wide CNV detection. The same segmented structure and analysis pipeline serve multiple purposes: detailed examination of high-confidence regions and broader survey of the entire genome, allowing the system to adapt to different clinical needs while maintaining versatility.
Data Source
AI summary
The present disclosure relates to genetic copy number variation (CNV) detection. Particularly, aspects are directed to sequencing nucleic acid obtained from a biological sample obtained from a subject to generate sequencing data. The sequence reads are ordered by mapping the sequence reads to a reference genome and stored in an ordered format, A global segmentation of the target region is performed based on the stored sequence reads and a set of segments of the target region is identified and used to determine a copy number variation (CNV) metric. A first status of a genetic condition for the subject is determined based on the CNV metric, and a report of the corresponding genetic condition screening test is determined based on the CNV metric and the status.